Real-Time Malfunction Detection of Maglev Suspension Controllers

Su-Mei Wang, You-Wu Wang, Yi-Qing Ni () and Yang Lu
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Su-Mei Wang: The Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, The Hong Kong Polytechnic University, Hong Kong
You-Wu Wang: The Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, The Hong Kong Polytechnic University, Hong Kong
Yi-Qing Ni: The Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, The Hong Kong Polytechnic University, Hong Kong
Yang Lu: The Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center, The Hong Kong Polytechnic University, Hong Kong

Mathematics, 2023, vol. 11, issue 19, 1-20

Abstract: This study aims to develop a track-side online monitoring system for malfunction detection in the suspension controllers of maglev trains during their in-service operation. The hardware module of the system includes two arrays of accelerometers deployed on an F-type rail and a data acquisition unit. The software module of the system consists of codes for three functions: (i) the identification of time intervals in relation to the passage of each suspension controller via synchrosqueezing transform; (ii) the extraction of a feature index (FI) sequence synthesized by modulating the response amplitude, frequency, and running speed; and (iii) the formulation of a Bayesian dynamic linear model for real-time malfunction detection in maglev suspension controllers. For verification of the proposed monitoring system and malfunction detection algorithm, full-scale tests have been conducted on an maglev test line using the devised system, where a maglev train was run at different speeds with malfunction occurring in the suspension controllers. The malfunction detection results of the proposed approach are exemplified via comparison with the recorded suspension gaps after the trial run of the maglev train. The fidelity of the results obtained using the extracted FI sequence and using the raw monitoring data are compared. The superiority of the proposed malfunction detection algorithm is also discussed via comparison with the results of the different train speeds.

Keywords: Bayesian dynamic linear model; maglev train; malfunction detection; suspension control system; track-side online monitoring (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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